Hierarchical modeling is a statistical approach that generally outperforms a conventional (one-level) analysis by using multiple level models. Specifically, hierarchical models can provide more reasonable and stable parameter estimates than conventional analytical approaches. In addition, this technique deals with problems of multiple comparisons and allows one to adequately model multilevel data within a hierarchical framework. In this project we are extending this technique to cancer research and genetic epidemiology through a combination of application, methodological development, and simulation studies. In particular, we are advancing this valuable technique by showing how one can use hierarchical modeling in genetic epidemiologic research to evaluate multiple candidate genes, and by providing to the scientific community software for undertaking hierarchical modeling. [unreadable])